evalml.pipelines.components.RandomForestRegressor

class evalml.pipelines.components.RandomForestRegressor(n_estimators=100, max_depth=6, n_jobs=- 1, random_state=0, **kwargs)[source]

Random Forest Regressor.

name = 'Random Forest Regressor'
model_family = 'random_forest'
supported_problem_types = [<ProblemTypes.REGRESSION: 'regression'>, <ProblemTypes.TIME_SERIES_REGRESSION: 'time series regression'>]
hyperparameter_ranges = {'max_depth': Integer(low=1, high=32, prior='uniform', transform='identity'), 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity')}
default_parameters = {'max_depth': 6, 'n_estimators': 100, 'n_jobs': -1}
predict_uses_y = False

Instance attributes

feature_importance

Returns importance associated with each feature.

needs_fitting

parameters

Returns the parameters which were used to initialize the component

Methods:

__init__

Initialize self.

clone

Constructs a new component with the same parameters and random state.

describe

Describe a component and its parameters

fit

Fits component to data

load

Loads component at file path

predict

Make predictions using selected features.

predict_proba

Make probability estimates for labels.

save

Saves component at file path

Class Inheritance

Inheritance diagram of RandomForestRegressor